A deep learning model to forecast cattle heat stress

The accurate forecasting of feedlot cattle heat stress is pivotal to improving animal welfare and reducing the economic losses associated with heat events. This study investigated how autonomously recorded cattle behaviour datasets can be leveraged to propose new approaches to cattle heat stress forecasting. Behaviour data acquired by accelerometer sensors in two experiments using mixed breed feedlot cattle were utilised, with duration of heavy breathing used to quantify heat stress. Two time-forecasting methods were proposed for predicting the average panting duration of a herd of cattle 24-hours into the future using meteorological data, animal characteristics and treatment factors as input. The first method utilised a simple deep learning framework based on Long-Short Term Memory networks, while the second applied traditional statistical methods to HLI, AHL and animal characteristic data. An optimisation algorithm was used to select which meteorological factors and how much historic data should be considered by the deep learning model. The experiments conducted emphasise that the deep learning approach was able to effectively model the lagged effect of weather on cattle heat stress. Consequently, it was found to be more accurate than traditional statistical methods and climate indices at forecasting the cattle heat response recorded in this study. A final experiment investigated the potential for using these time forecasting models to provide decision support to producers. This preliminary study thus emphasises that autonomously derived behaviour datasets and deep learning have the potential to improve animal welfare and productivity.